Comparison of the Effect of Word Normalization on Naïve Bayes Classifier and K-Nearest Neighbor Methods for Sentiment Analysis

Abstract

In the pre-processing stage of sentiment analysis, there are several essential steps, one of which is word normalization, which is converting non-standard words into standard words. However, some research on sentiment analysis generally does not go through the word normalization stage, which can affect accuracy. This study aims to compare the effect of word normalization on the Naive Bayes Classifier and K-Nearest Neighbor methods for sentiment analysis of public opinion on the Agency Social Security Administrator for Health (BPJS Kesehatan). Gathering the data, labeling it, pre-processing it with two different scenarios, word weighting it with TF-IDF, classifying it using Naive Bayes Classifier and K-Nearest Neighbor, and lastly computing the accuracy of the Confusion Matrix are the steps that are involved. As a result of these discovered fact, the most superior accuracy results are obtained by the Naive Bayes Classifier method 1st scenario, namely by using word normalization at the pre-processing stage and getting an accuracy of 87.14%. This research shows that the Naive Bayes Classifier method with word normalization produces better accuracy, precision, recall, and F1-score.